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Record W4205488647 · doi:10.3390/jrfm15010037

The Impact of CEO Duality and Financial Performance on CSR Disclosure: Empirical Evidence from State-Owned Enterprises in China

2022· article· en· W4205488647 on OpenAlexvenueno aff
Cosmina Lelia Voinea, Fawad Rauf, Khwaja Naveed, Cosmin Fratostiteanu

Bibliographic record

VenueJournal of risk and financial management · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsnot available
Fundersnot available
KeywordsCorporate social responsibilityBusinessDual (grammatical number)Corporate governanceAccountingContext (archaeology)Chief executive officerChinaQuality (philosophy)StakeholderState ownedStakeholder theoryFinancePublic relationsEconomicsMarket economyManagement

Abstract

fetched live from OpenAlex

This paper studies the effects of a firm’s financial performance (FP) and chief executive officer’s (CEO) duality on the quality of corporate social responsibility (CSR) disclosure in the context of state-owned enterprises (SOEs) among Chinese A-share-registered companies. The results depict a negative relationship between CEO duality and CSR disclosure. Our results demonstrate that better-performing firms disclose CSR information more frequently and of higher quality compared with firms with poor financial performance. This role of financial performance in the quality of CSR disclosure is generally valuable in public enterprises; however, it is relatively sluggish in state-owned enterprises the outcomes indicate that the dual leadership structure reduces assessments and renders CEOs less liable to their stakeholders. Therefore, this study offers valuable information and details for regulators to improve corporate governance and CSR from the perspective of stakeholder theory.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.146
Threshold uncertainty score0.500

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.024
GPT teacher head0.279
Teacher spread0.255 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations48
Published2022
Admission routes1
Has abstractyes

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